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Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation
Yifan Song, Fenglin Yu, Yihong Luo +4
Incomplete node features are ubiquitous in real-world scenarios such as user profiling and cold-start recommendation, which severely hinders the practical deployment of graph learn…
Reinforcing Diffusion Models by Direct Group Preference Optimization
Yihong Luo, Tianyang Hu, Jing Tang
While reinforcement learning methods such as Group Relative Preference Optimization (GRPO) have significantly enhanced Large Language Models, adapting them to diffusion models rema…
Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls
Yihong Luo, Shuchen Xue, Tianyang Hu +1
The pursuit of efficient and controllable high-quality content generation remains a central challenge in artificial intelligence-generated content (AIGC). While one-step generators…
Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs
Yuhan Chen, Yihong Luo, Yifan Song +3
Despite extensive research efforts focused on OOD detection on images, OOD detection on nodes in graph learning remains underexplored. The dependence among graph nodes hinders the…
Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification
Yihong Luo, Yuhan Chen, Siya Qiu +5
Graph Neural Networks (GNNs) have shown superior performance in node classification. However, GNNs perform poorly in the Few-Shot Node Classification (FSNC) task that requires robu…
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity
Yuhan Chen, Yihong Luo, Jing Tang +4
Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion…